Method and system for applying data retention policies in a computing platform

ABSTRACT

Systems and methods for a multitenant computing platform. Original data is generated through operation of a computing platform system on behalf of an account of the computing platform system, and the original data is moderated according to a data retention policy set for the account. The moderated data is stored at the computing platform system. The computing platform system moderates the generated data by securing sensitive information of the generated data from access by the computing platform system, and providing operational information from the generated data. The operational information is accessible by the computing platform system during performance of system operations.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No. 14/974,312, filed 18 Dec. 2015, which is a continuation of U.S. patent application Ser. No. 14/793,435, filed 7 Jul. 2015, which claims the benefit of U.S. Provisional Application Ser. No. 62/021,645, filed on 7 Jul. 2014, all of which are incorporated in their entirety by this reference.

TECHNICAL FIELD

This invention relates generally to the data management field, and more specifically to a new and useful method and system for applying data retention policies in the data management field.

BACKGROUND

Data analytics are an important part of running a data driven computing platform. However, there are many cases where the data is inappropriate for storage. In some cases, the information is sensitive and an operator would not want to store such information. Storing such information may violate the trust of involved parties or create an information liability. In some cases, the data cannot be stored to maintain compliance with regulations. For example, personal medical information may not be allowed to be stored when building a HIPAA compliant application. Thus, there is a need in the data management field to create a new and useful method and system for applying data retention policies in a computing platform. This invention provides such a new and useful method and system.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a flow diagram of a method of a preferred embodiment;

FIG. 2 is a schematic representation of an exemplary implementation of a preferred embodiment;

FIG. 3 is a schematic representation of an exemplary implementation of a preferred embodiment;

FIG. 4 is a flow diagram of a method of a preferred embodiment;

FIG. 5 is a flow diagram of a method of a preferred embodiment; and

FIG. 6 is an architecture diagram of system of a preferred embodiment.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The following description of preferred embodiments of the invention is not intended to limit the invention to these preferred embodiments, but rather to enable any person skilled in the art to make and use this invention.

1. METHOD

As shown in FIG. 1, a method for controlling data of a preferred embodiment can include setting of a data retention policy of an account S110, generating data through the operation of a computing platform S120, moderating data of the account according to the data retention policy of the account S130, and storing the moderated data S140. The method functions to provide a mechanism through which a data-driven computing platform can accommodate a wide variety of data retention polices while serving number of different accounts. The method may be used to define how data is stored long term. Additionally or alternatively, the method may be used to provide data “deletion” capabilities to an account wherein the operational significance of the data is preserved for other parties (e.g., a computing platform operator). The method is preferably used in a multitenant computing platform, wherein each account, sub-account or other data scope may have individually assigned data retention polices.

The computing platform is preferably data-driven in the sense that the accumulation of data is used in subsequent processes of the platform—at least one operational aspect depends on accurate and exhaustive collection of data. In one case the data of the computing platform is metered/measured for each account and used in regulating usage of an account. In computing platform where the usage is a factor of billing, the data history for an account must be accurate to calculate fees. One objective of the method is to enable such data-driven behavior, while simultaneously enabling data protection that may otherwise conflict with the notion of metering usage.

In one particular implementation, the computing platform is a communication platform and more specifically a communication application platform such as the one described in U.S. Pat. No. 8,306,021, issued 6 Nov. 2012, which is incorporated in its entirety by this reference. A communication platform may have complicated billing models that can depend on the count of communications, the source and/or destination of communication, the type of communication, the duration of communication, the events and media processes associated with the communication (e.g., text to speech services, speech detection services, transcription services, recording services, etc.), rate and threshold billing variables, and other suitable factors. Such complicated billing models may preclude the on-demand calculation of itemized billing per communication or data record. The method can address the requirement of accounting while preserving the data in a private manner. Herein, the communication platform may be used as an exemplary platform, but any suitable computing platform may similarly apply the method for controlling data.

Block S110, which includes setting of a data retention policy of an account S110, functions to receive a signal that defines how at least a subset of data should be retained with the system. A data retention policy is preferably set by an account holder. The data retention policy is preferably received from the account holder. In one variation, the data retention policy is pre-defined. The policy retention policy could be globally pre-set for all data generated in association with the account. The policy retention policy may alternatively be defined for a sub-set of account data. For example, a policy retention policy may be mapped to a sub-account of the account, to a type of data (e.g., data generated during a voice communication or data generated during a message communication), or any suitable data scope. For example, data associated with SMS and MMS messaging may not be set for “deletion” while voice communication data is kept in an original format. Similarly, the data retention policy for communication with a first endpoint (e.g., phone number) may be different for data retention policies for a second endpoint. In one variation, an administrator of an account may specify the data retention policy/policies through an administrator control panel user interface. In another variation, the data retention policy may be set in response to a developer API request. A data retention policy may alternatively be defined in any suitable manner.

Data retention policy may additionally or alternatively be specified on-demand. The data retention policy can be defined in directives during operation of the computing platform. In the communication platform implementation, a data retention policy may be selectively changed for part or all of a call. The data retention policy directives may provide commands to initiate pause, end, or otherwise change the data retention policy. A data retention policy may be initiated in a first instance and then terminated in a second instance. Any data generated or associated with the time period between the first and second instances is preferably processed according to the data retention policy. Data outside of those two instances may be processed according to the default data retention policy or any original data retention policy. In one example, a user may be placing a call to a banking customer support phone system. While most of the communicated information is not sensitive data, a portion of the call may require the customer to enter personal information such as a credit card. The data retention policy may be elevated to a higher level of data protection during this process to prevent such data being retained and accessible in data logs.

Additionally or alternatively, a specific request to apply data retention to one or more data elements may be received and processed. In this variation, specific data records can be selectively targeted for particular data retention policy compliance.

A data retention policy preferably defines actions to take on data prior to storing or warehousing the data. In one variation, there are preferably at least two data retention policies. A first data retention policy is a passive data retention policy that preserves the data in an original and raw format. Such a data retention policy is preferably a default data retention policy, and no action is preferably taken on the data. The passive data retention policy can alternatively be described as a lack of a data retention policy. The other forms of data retention policy are preferably transformative data retention policies that result in some change or transformation of the data. A transformative data retention policy preferably removes or secures sensitive information while creating some mechanism through which the computing platform can accomplish data-driven operations.

A transformative data retention policy will preferably take some form of a data retention action on the data during moderation of the data. A data retention action can include data redaction/censoring, data classifying/bucketing, data aggregating, data encryption, partial deletion, and/or any suitable approach to data protection. Within the computing platform varying levels of data retention policies may be defined wherein different levels of data retention may have differing degrees of data destruction/preservation. Additionally, different forms of data transformation may be applied to different data fields. In a computing platform, the data stored may follow a substantially defined schema and the forms of transformations that should be applied can be customized for each field. For example, some fields may not be used for data-driven processes and can be deleted, while other fields may be suitable for a form of redaction, while other fields may be better suited for data classifying or bucketing.

A transformative data retention policy may additionally include one or more defined temporal properties. One temporal property may define how long the data may be retained before the data is moderated and transformed. One account may maintain the original raw data for 30 days and after 30 days transform the data. Another account may have no temporary need for the data and transform data directly after completing active use (e.g., during initial warehousing of the data). A second temporal property may be a backup time window that defines how long the original data is preserved in addition to the transformed data before deletion. In this variation, the method enables the capability to undo or reverse the transformation of a data retention policy. For example, a backup time window property for one account of 24 hours will allow any data transformation or deletion request to be undone for up to 24 hours. In one implementation, deleted or transformed data may be shown in a special folder within an administrator control panel until the time window is up. In another example, a backup time window property for another account can be set to zero seconds, and any data transformations or deletion requests are effective immediately and cannot be reversed.

The interface through which a data retention policy is received can be through an Application Program Interface, a configuration file, a user interface (e.g., in an administrator control panel), or any suitable interface. The manner in which a data retention policy is defined may be achieved through various approaches. In a first variation, a transformative data retention policy can be selected from a set of offered transformative data retention policy options. For example, an account may be able to set up an application within a communication application platform, and in the settings of that application select a default of no data transformations, a pre-defined redaction process, or a custom encryption data retention process. In another variation, a data retention policy may be specifically defined. A schema or configuration file may be provided that defines how data retention should be applied. The data retention can be specified specifically for different data attributes. Particular types of data retention actions may be directed to particular data types, data conditions (e.g., if a data field satisfies a specified condition enact a data retention action), data fields, or other suitable aspects. Alternatively, any suitable approach may be used to define the data retention policy. In another variation, the type of data retention policy may be defined based on the type of account.

Block S120, which includes generating data through the operation of a computing platform S120, functions to produce data within a system. The data produced is preferably data produced as a result of the accounts, users, or other entities using the multi-tenant computing platform. The data can be data logs, API request/response records, captured packets (PCAP files), form data input, user generated data, generated or obtained media (e.g., audio, images, video, etc.), and/or any suitable type of data. The data may be accessible to an account holder for any suitable use. For example, a customer support phone system built in a communication application platform may include event logs that include meta data about the calling phone number, the called phone number, the duration of the call, media recordings made during the call, DTMF input, and other suitable information. An account holder is preferably a developer account or administrator account, which may build different analytics or tools that leverage a portion of the generated data. For example, a history of a customer support agent could be generated by polling the data source of the communication application platform. Since the computing platform may be built as a general set of functionality to serve a wide variety of parties, there may be particular use cases to which this data logging behavior is not ideal or possibly prohibitive. For example, if phone system builds a tool where users enter their social security number, the data logs will automatically create a record of callers' social security numbers. The administrator of this system may not want to be liable for having access to such sensitive data. In another example, a health care system may end up storing personal identifiable information in the data, which may cause HIPAA compliance issues and so such automatic data logging may ordinarily prevent such a use case. The method of the preferred embodiment can preferably address such scenarios.

While the data generated may be the result of building a generic tool, the computing platform may additionally partially depend on information of the data. The computing platform in which the data is generated may be an at least partially closed system with operations that are outside of the control or direction of an account holder

there are preferably components of the computing platform to which an account holder/user of the platform will not have visibility. The computing platform is preferably multitenant, wherein multiple account holders will share the use of the computing platform while maintaining distinct and substantially independent applications/services. The partially closed portions of the platform can include the system orchestration system, usage/analytics tools, billing engine, business intelligence tools, a platform operations system (e.g., the platform operations system 270 of FIG. 2) and/or any suitable system. In some implementations, the platform operations system performs at least one of platform orchestration, usage metering, analytics, billing, and business intelligence. In some implementations, the platform operations system performs usage/analytics. Such system (or systems) can depend on the data generated in connection with an account holder. The data retention policy management method described herein may function to enable data protection without hindering or preventing such operations.

The data generated may have different stages in the data life cycle. The data is preferably generated as a result of some event relating to an account, sub-account, user action of an application, service action, or other suitable event. The data may have a period of being in-flight wherein inflight data is actively stored for use within some operation. For example, data generated during a phone call may be in-flight for the duration of the call. An SMS or MMS message may have in-flight data for the duration to complete transmission. Alternatively, there may be a concept of a conversation wherein the data is in-flight for the duration of the messaging conversation. After active use, the data may be moved to a temporary storage system prior to being transmitted for data warehousing. Data warehousing will preferably be used to store the data for long duration. It is between in-flight state and the data warehousing that blocks S130 preferably occurs, but Block S130 may alternatively occur at any suitable time. The data may additionally or alternatively include any alternative states.

Block S130, which includes moderating data of the account according to the data retention policy of the account, functions to exercise the actions defined by the data retention policy. As described above, the data retention policy is preferably exercised after active use and prior to long term storage for data records. The data retention policy may alternatively be applied to any new data records at a periodic interval, be applied immediately as data is generated, or at any suitable time. The conditions in which the data retention policy is exercised are preferably dependent on the data retention policy configuration of an account. More generally, the moderation of the data depends on the data retention policy defined for the data scope (e.g., sub-account data, user data, etc.). In the case where the data retention policy is to take no action, then the data is preferably stored in a raw and unaltered state. In the case where the data retention policy is a transformative data retention policy, the data will be augmented according to the defined actions. There may be different variations on how data is augmented or moderated. Some preferred variations might include data redaction processing, data classifying, data aggregating, data encrypting, partially deleting, and/or any suitable approach to data protection.

Redaction processing functions to remove elements of the data that are sensitive. The redaction processing can effectively censor data so as to put it in a form suitable for storage. Redaction processing additionally can preserve a subset of the data content. Preferably, the information in the data that is desired by the computing platform can be preserved while a subset or all of the remaining data is removed. Redaction processing is preferably applied to data fields or properties where the semantics or pattern of the data is understood sufficiently to differentiate between what should be kept and what should be removed. In one case, phone numbers may be an element of a data record. Phone numbers may provide personally identifying information as they often map back to an individual. However, a communication platform may depend on knowing the country and area codes of phone numbers during billing of an account. Accordingly, the country and area code are preferably preserved while the remaining four digits are censored. In one variation, redaction processing may include automatically detecting a pattern and applying censorship to the pattern. Automatic detection may be useful in situations where a fixed rule cannot be defined to specify where and when content will need to be augmented. Credit card numbers, social security numbers, and account numbers, addresses, and other suitable forms of information may be detected and automatically removed from the data. Such type of content may appear in various places, when data matches those patterns it may be automatically removed.

Data classifying functions to abstract or bucket the data content to remove details of the original information. The data classifying preferably includes abstracting up the level of information in the original data. One approach is to classify content into a higher-level abstraction. For example, geo-location data may be generalized from precise geo-location data to general location information such as zip code, region, city, state or country. As another alternative, data metrics may be bucketed from precise measurements into ranges. For example, a data metric measuring the duration of a call may be changed from second-level precision to minute level precision.

Data aggregating functions to create a distinct data record that is the cumulative combination of previous data records. The precise metrics of a data record can be maintained but only in combination with a set of other data records. The individual metric is preferably deleted or censored. For example, the total duration of a phone call may be aggregated into total duration of all phone calls for an account, however the duration of the individual call cannot normally be obtained.

Encrypting data functions to cryptographically transform the data. Encrypting data preferably depends on an account-defined key. Encrypting data preferably includes receiving an encryption callback reference, determining the data content to be encrypted, transmitting the original content to the encryption callback reference (e.g., the encryption callback 281 of FIG. 2), receiving encrypted data content and using the encrypted data content in place of the original content. The encryption callback reference is preferably a callback URI operated by the account holder. HTTP, SPDY, or any suitable application layer protocol may be used to communicate the original data to the callback URI. The account holder will receive the original data and can use a self-defined encryption algorithm and key to encrypt the data, which is then returned for storage. The encryption allows only the account holder to access the contents of the data. Encryption can be used if the data should be secured but not deleted permanently. Encryption may be used in combination with redaction classification, aggregation, or any suitable data transformation. Redaction, classification, and aggregation may enable system dependent information to be preserved while removing sensitive data. For example, if phone numbers are encrypted, the account holder may be able to decrypt the encrypted version to view the data. However, since a communication application platform may depend on the country and area code of that data, a redacted copy of that data property may additionally be stored.

A data augmentation may additionally include a partial deletion of data, wherein some data fields or whole data records may be deleted. Some subset of data types or data parameters may be fully deletable. Such fields may include customer defined data fields (e.g., data tags or metadata).

Block S140, which includes storing the moderated data, functions to store the moderated data. The moderated data can be stored in any suitable manner. As described above, for encrypted data. A second form of data transformation may be stored for some all parts of the encrypted data. The stored moderated data may be used for various system operations such as scaling infrastructure, metering account usage, billing for account usage, informing business decisions, acquiring assets, or any suitable data-driven decision. The policy transformed data is preferably applied to any location where data is stored such as in a data warehouse, log files, media records, and/or any suitable location.

The method can additionally facilitate various data related functionality. Such functionality may be enabled on secured data despite the original data being too sensitive to normally allow such functionality. As a primary functionality, account usage and analytics can be provided. Data aggregation, classification, and selective redaction can preserve some level of information that can provide insight into patterns. Such data preservation may additionally be applied to enable fraud detection, error detection, or general event pattern detection. Within the computing platform, the data information may be used in making decisions related to platform administration, orchestrating a cluster or distributed computing system, allocating/deallocating resource, pricing, and/or other operational factors of the computing platform. The systematic approach to data retention policies may additionally provide an audit trail of data management for an account, which can be used to show data compliance in various situations.

In one preferred implementation, the method is applied to a communication platform that can facilitate synchronous communication such as voice, video, screen sharing, virtual reality and/or any suitable media stream. The synchronous communication may use PSTN, SIP, WebRTC, IP-based protocols, or any suitable communication protocols. The communication platform may additionally or alternatively facilitate asynchronous communication such as SMS, MMS, or IP based messaging. As shown in FIG. 2, a communication (e.g., a communication requested by the communication request 211 of FIG. 2) will be executed on the communication platform (e.g., by the communication system 210 of FIG. 2). Various events during the communication such as the communication request, media generated during the communication, input received during the communication (e.g., DTMF input), and a summary of the communication after it completes may all be exemplary data records generated (e.g., by the communication system 210 of FIG. 2) in association with the communication. While the communication is active, the data is preferably stored in in-flight data storage (e.g., the in-flight data storage 220 of FIG. 2) (e.g., active data storage). Data may be mutable and possibly incomplete at this state. Once the call is completed, the associated data may be moved to a post-flight data storage system (e.g., the post-flight data storage 230 of FIG. 2). The post-flight data storage functions as a temporary data storage solution prior to being moved to a data warehousing solution (e.g., the data warehouse system 260 of FIG. 2). The post flight data storage may additionally provide faster real-time data information for particular use-cases. Periodically (based on a time period or satisfying some condition), the post-flight data is onboarded into the data warehousing system (e.g., the data warehouse system 260 of FIG. 2). A data retention policy engine (e.g., the data retention policy engine 251 of the data manager 250 of FIG. 2) preferably facilitates the onboarding process by exercising data retention policies (e.g., the data retention policy 252 of FIG. 2) that are assigned to the various data records. Data for an account that lacks a defined data retention policy will be onboarded with no transformation. Data for an account that has a transformation data retention policy will be transformed according to the data retention policy.

In one example, form of a data retention policy a call record may have the following actions applied call record fields: the “to” field is redacted to exclude last four digits, the “from” field is redacted to exclude last four digits, application URL field is deleted, duration field is bucketed into five minute buckets, time field is bucketed to only show events by hour, associated account identifier is kept, and a price field is deleted or bucketed. A location field may be abstracted to only show city information. Call recordings may be deleted or encrypted through an account controlled cryptographic key.

In some implementations, the communication platform includes the communication system 210, the in-flight data storage 220, the post-flight data storage 230, the data manager 250, the data retention policy engine 251, the data warehouse 260, the data retention policy 252, and the platform operations system 270, and the account holder system 280 is external to the communication platform.

In some implementations, the communication platform (e.g., the communication platform 200 of FIG. 2) includes the communication system 210, the in-flight data storage 220, the post-flight data storage 230, the data manager 250, the data retention policy engine 251, the data warehouse 260, the data retention policy 252, and the platform operations system 270, and the account holder system 280. In some implementations, the platform operations system 270 is external to the communication platform. In some implementations, the data warehouse system 260 is external to the communication platform. In some implementations, the data retention policy engine 251 is constructed to perform redaction, data classifying, data aggregating, and encrypting. In some implementations, the data warehouse system 260 is included in an account holder system (e.g., the account holder system 280), and the communication platform includes information to access data in the data warehouse system 260.

2. MULTI-TENANT COMPUTING PLATFORM SYSTEM

As shown in FIG. 3, a multi-tenant computing platform system 300 includes a computing system 310, an in-flight data storage system 320, a post-flight data storage system 330, a data manager 350, a data retention policy engine 351, a data warehouse system 360, and a platform operations system 370. The account holder system 380 is external to the computing platform 300. The computing system 310 includes an accounting system 312, a data retention policy API 313, and a computing service API 314.

In some implementations, the computing platform system 300 includes the account holder system. In some implementations, the platform operations system is external to the computing platform system. In some implementations, the data warehouse system is external to the computing platform system. In some implementations, the data retention policy engine is constructed to perform redaction, data classifying, data aggregating, and encrypting. In some implementations, the data warehouse system is included in an account holder system (e.g., the account holder system 380), and the computing platform system includes information to access data in the data warehouse system.

In some implementations, the computing platform system 300 is similar to the computing platform described above for FIG. 1. In some implementations, the in-flight data storage system 320 is similar to the in-flight data storage 220 of FIG. 2. In some implementations, the post-flight data storage system 330 is similar to the post-flight data storage 230 of FIG. 2. In some implementations, the data manager 350 is similar to the data manager 250 of FIG. 2. In some implementations, the data retention policy engine 351 is similar to the data retention policy engine 251 of FIG. 2. In some implementations, the data warehouse system 360, is similar to the data warehouse system 260 of FIG. 2. In some implementations, the platform operations system 370 is similar to the platform operations system 270 of FIG. 2.

The system 300 is communicatively coupled to the external system 380 via the data retention policy API 313 and the computing service API 314 of the computing system 310.

In the embodiment of FIG. 3, the external system 380 is a system of an account holder of an account (e.g., an account of the account system 312) of the computing platform system 300. In some implementations, external systems include a system of an application developer that provides an application to users of the external system. In some implementations, external systems include a system of a service provider that provides a service to users of the external system. In some implementations, external systems include a communication endpoint.

In some implementations, the computing system 310, the in-flight data storage system 320, the post-flight data storage system 330, the data manager 350, the data retention policy engine 351, the data warehouse system 360, and the platform operations system 370 are implemented as a server device. In some implementations, the computing system 310, the in-flight data storage system 320, the post-flight data storage system 330, the data manager 350, the data retention policy engine 351, the data warehouse system 360, and the platform operations system 370 are implemented as a plurality of server devices communicatively coupled to each other (e.g., a computing cluster).

The computing system 310 functions to provide any suitable computing service (e.g., a service provided via the computing service API 314).

In some implementations, the computing system 310 includes an account system (e.g., 312), which functions to allow distinct accounts to use the computing system 310. An account is preferably operated by a developer or application provider that builds an application or service that utilizes the computing system 310. For example, in an implementation in which the computing system 310 is a communication system, an account holder of an account may build a call center application that uses the computing system 310 to direct customers to customer service representatives. Alternatively, the account holder of an account may be an end user of an endpoint (e.g., phone number or SIP address) that uses the computing system 310 to provide some service. For example, an end user may use the computing system 310 to dynamically direct incoming calls to ring multiple destinations until the first device picks up. Any suitable account hierarchy or division may be used. For example, an account may include subaccounts, which run different instances of an application with unique configuration. The accounts additionally have specific authentication credentials. API requests and communication is preferably scoped to a particular account. Accordingly, a data retention policy provided by one account can be stored and associated with the account.

The data retention policy API 313 is preferably a set of data retention policy API calls and/or resources that can be used in the setting, editing, and reading of one or more data retention policies. In some implementations, an account is preferably limited with privileges to interacting with data retention policies associated with the account.

The data retention policy API 313 is preferably part of a RESTful API but may alternatively be any suitable API such as SOAP or custom protocol. The RESTful API works according to an HTTP request and response model. HTTP requests (or any suitable request communication) to the computing platform 300 preferably observe the principles of a RESTful design. RESTful is understood in this document to describe a Representational State Transfer architecture as is known in the art. The RESTful HTTP requests are preferably stateless, thus each message communicated contains all necessary information for processing the request and generating a response. The API service can include various resources, which act as API endpoints, which act as a mechanism for specifying requested information or requesting particular actions. The resources can be expressed as URI's or resource paths. The RESTful API resources can additionally be responsive to different types of HTTP methods such as GET, PUT, POST and/or DELETE.

3. METHOD OF FIG. 4

The method 400 of FIG. 4 includes setting a data retention policy (e.g., 352 of FIG. 3) of an account (e.g., an account of the account holder system 380) at the computing platform system (e.g., the system 300) (process S410); generating data (e.g., the original data 340) through operation of the computing platform system (e.g., 300) on behalf of the account (process S420); moderating the generated data of the account according to the data retention policy of the account (process S430); and storing the moderated data (e.g., the policy compliant data 354 of FIG. 3) (process S440). The computing platform system moderates the generated data by: securing sensitive information of the generated data (e.g., 340) from access by the computing platform system (e.g., 300); and providing operational information from the generated data, the operational information being accessible by the computing platform system (e.g., 300) during performance of system operations (e.g., by the platform operations system 370).

In some implementations, the moderated data is stored at a data warehouse system (e.g., 360 of FIG. 3).

In some implementations, the method 400 includes: accessing, at the computing platform system (e.g., 300) (e.g., by using the platform operations system 370) the moderated data (e.g., 354) stored at the data warehouse system (e.g., 360) (process S450); and performing (e.g., by using the platform operations system 370) at least one system operation by using the accessed moderated data (process S460). In some implementations, system operations include at least one of usage analytics, business intelligence operations, infrastructure scaling operations, metering account usage, billing for account usage, fraud detection, error detection, general event pattern detection, platform administration operations, allocating resources, deallocating resources, cluster management operations, and auditing operations.

In some implementations, the multi-tenant computing platform system 300 performs the processes S410-S440. In some implementations, the multi-tenant computing platform system 300 performs the process S450. In some implementations, the multi-tenant computing platform system 300 performs the process S460.

In some implementations, the computing system 310 performs the process S410. In some implementations, the policy API 313 performs the process S410. In some implementations, the computing system 310 and the policy API 313 perform the process S410. In some implementations, the computing system 310 performs the process S410 responsive to a request received via the policy API 313. In some implementations, the computing system 310 performs the process S410 responsive to a response received via the policy API 313.

In some implementations, the computing system 310 performs the process S420.

In some implementations, the data retention policy engine 351 performs the process S430.

In some implementations, the data retention policy engine 351 performs the process S440. In some implementations, the data warehouse system 360 performs the process S440. In some implementations, the system 300 stores the moderated data (e.g., the moderated data 354 of FIG. 3) in a storage device (e.g., the storage medium 605 of FIG. 6) of the system 300.

In some implementations, the system 300 stores the data retention policy (e.g., the data retention policy 352 of FIG. 3) in a storage device (e.g., the storage medium 605 of FIG. 6) of the system 300.

In some implementations, the platform operations system 370 performs the process S450. In some implementations, the platform operations system 370 performs the process S460.

In some implementations, the process S410 is similar to the process S110 of FIG. 1. In some implementations, the process S420 is similar to the process S120 of FIG. 1. In some implementations, the process S430 is similar to the process S130 of FIG. 1. In some implementations, and the process S440 is similar to the process S140 of FIG. 1.

3.1 Setting a Data Retention Policy

In some implementations, the process S410 functions to control the multi-tenant computing platform system 300 to set a data retention policy of an account (e.g., an account of the account system 312) at the computing platform system 300. In some implementations, the data retention policy is set as described above for S110 of FIG. 1. In some implementations, the data retention policy is similar to at least one of the data retention policies described above for S110 of FIG. 1.

In some implementations, the computing system 310 receives the data retention policy (e.g., 352) in a data retention policy message provided by an external system (e.g., the external account holder system 380), and responsive to the data retention policy message, the computing system 310 sets the data retention policy (e.g., 352) at the system 300 in association with an account identifier specified by the data retention policy message (e.g., an account of the account holder system 380). In some implementations, the computing system 310 receives the data retention policy (e.g., 352) via the data retention policy API 313.

In some implementations, the computing system 310 receives the data retention policy (e.g., 352) via an administrator control panel user interface provided by the system 300 (e.g., provided to the external account holder system 380).

In some implementations, the computing system 310 accesses a configuration file provided by an external account holder system (e.g., 380), and the configuration file defines the data retention policy 352.

In some implementations, the computing system 310 receives the data retention policy (e.g., 352) by processing a configuration file. In some implementations, the computing system 310 receives the data retention policy (e.g., 352) by processing a configuration file of an account holder of an account at the system 300 (e.g., an account associated with the external system 380).

In some implementations, the data retention policy is specified on-demand. In some implementations, the data retention policy is defined in directives during operation of the computing system 310. The computing system 310 processes such directives which set the data retention policy at the system 300.

In some implementations in which the system 300 is a communication platform system, the data retention policy is selectively changed for part or all of a call, as described above for S110. In some implementations in which the system 300 is a communication platform system, the data retention policy is selectively changed at least a portion of a communication session (e.g., a telephony voice communication) in a manner similar to that which is described above for S110.

In some implementations, the data retention policy (e.g., 352) is received from an external account holder system (e.g., 380), and the policy is received with a request to apply the policy to one or more specified data elements. In some implementations, specific data records are selectively targeted for particular data retention policy compliance. In some implementations, the data retention policy is a transformative data retention policy as described above for S110. In some implementations, the data retention policy is a transformative data retention policy that secures sensitive information while providing the system 300 with information for performing data-driven system operations. In some implementations, the transformative data retention policy defines at least one data retention action to be performed on the data during moderation of the data. In some implementations, the transformative data retention policy defines at least one data retention action to be performed on the data during moderation of the data, and at least one temporal property (e.g., a temporal property as described above for S110).

In some implementations, the computing system 310 sets the data retention policy by storing the data retention policy 352 in a storage medium of the system 300 (e.g., the storage medium 605 of FIG. 6) in association with the account identifier of the data retention policy message. In some implementations, the computing system 310 sets the data retention policy by storing the data retention policy 352 and the account identifier in a data retention policy data structure of the storage medium of the system 300 (e.g., the storage medium 605 of FIG. 6). In some implementations, the computing system 310 sets the data retention policy by storing a data retention policy data structure of the storage medium of the system 300 (e.g., the storage medium 605 of FIG. 6), the data retention policy data structure including the account identifier and a link to a storage location of the data retention policy 352.

3.2 Generating the Data

In some implementations, the process S420 functions to control the multi-tenant computing platform system 300 to generate data (e.g., the original data 340) through operation of the computing platform system (e.g., 300) on behalf of the account (e.g., an account of the account system 312). In some implementations, the process S420 functions to generate data within the system 300. In some implementations, the generated data (e.g., 340) is data that is produced as a result of accounts (of the system 300), users or other entities using the multi-tenant computing platform system 300.

In some implementations, the computing system 310 generates the data (e.g., the data 340) responsive to a computing request (e.g., the computing request 311) provided by an external system (e.g., the account holder system 380) and received by the computing system 310 via the computing service API (Application Program Interface) 314.

In some implementations, the generated data includes at least one of data logs, API request records, API response records, captured packets, form data input, user generated data, generated media, and obtained media.

The data is similar to the generated data described above for S120 of FIG. 1).

3.3 Moderating the Generated Data

In some implementations, the process S430 functions to control the multi-tenant computing platform system 300 to moderate the generated data of the account according to the data retention policy of the account. In some implementations, the data manger 350 receives the generated data (e.g., 340) from the computing system 310. In some implementations, the data manger 350 receives the generated data (e.g., 340) from the in-flight data storage system 320. In some implementations, the data manger 350 receives the generated data (e.g., 340) from the post-flight data storage system 330.

In some implementations, the data manager 350 moderates the received generated data according to the data retention policy 352. In some implementations, the data retention policy engine 351 of the data manger 350 moderates the received generated data according to the data retention policy 352. In some implementations, the data manager 350 receives the policy 352 from the computing system 310. In some implementations, the data manager 350 receives the policy 352 from an external system (e.g., the external account holder system 380). In some implementations, the data manager 350 moderates the data as described for S130 of FIG. 1.

In some implementations, the data retention policy engine 351 stores the data retention policy 352. In some implementations, the data retention policy engine 351 manages the data retention policy 352.

In some implementations, the data manager 350 moderates the received generated data by performing actions defined by the data retention policy 352. In some implementations, actions include at least one of data redaction, data censoring, data classifying, data bucketing, data aggregating, data encryption, and partial deletion.

In some implementations, the data retention policy (e.g., 352) defines actions performed by the computing platform system 300 on the data (e.g., 340) to secure the sensitive information prior to storing the data in a data warehouse (e.g., 360) of the computing platform system, and moderating data includes performing the actions defined by the data retention policy. In some implementations, actions include at least one of data redaction, data censoring, data classifying, data bucketing, data aggregating, data encryption, and partial deletion.

In some implementations, the data manager 350 performs redaction as described above for S130 of FIG. 1. In some implementations, data redaction includes automatically detecting and removing at least one of a credit card number, social security number, account number, and address from the data (e.g., 340).

In some implementations, the data manager 350 performs data classifying as described above for S130 of FIG. 1. In some implementations, data classifying includes replacing data with a generalized representation of the data.

In some implementations, the data manager 350 performs data aggregating as described above for S130 of FIG. 1. In some implementations, data aggregating includes replacing metrics of data with an aggregated representation of the metrics of data.

In some implementations, the data manager 350 performs data encryption as described above for S130 of FIG. 1. In some implementations, data encryption includes determining an encryption callback reference (e.g., 381) for the data, transmitting the data to an external system (e.g., 380) of the encryption callback reference (e.g., 381), and replacing the data (e.g., the original data 340) with encrypted data provided by the external system of the encryption callback reference, wherein the account is an account for the external system (e.g., 380). In some implementations, the encryption allows only an account holder of the account (e.g., an account of the accounting system 312 that corresponds to the data retention policy) to access the encrypted sensitive information.

In some implementations, the data manager 350 performs partial deletion as described above for S130 of FIG. 1.

In some implementations, the data manager 350 moderates the received generated data 340 after active use of the generated data by the computing system 310, and prior to long term storage of the data (e.g., in the data warehouse 360). In some implementations, the data manager 350 moderates newly generated data (e.g., 340) at a periodic interval. In some implementations, the data manager 350 moderates newly generated data (e.g., 340) immediately as the data is generated.

In some implementations, moderating the generated data (process S430) includes securing sensitive information of the generated data (e.g., 340) from access by the computing platform system (e.g., 300); and providing operational information from the generated data, the operational information being accessible by the computing platform system (e.g., 300) during performance of system operations (e.g., by the platform operations system 370). In some implementations, the data manager 350 secures sensitive information of the generated data. In some implementations, the data retention policy engine 351 secures sensitive information of the generated data. In some implementations, the data manager 350 provides the operational information from the generated data. In some implementations, the data retention policy engine 351 provides the operational information from the generated data.

In some implementations, securing sensitive information includes at least one of redacting, removing, censoring and encrypting of the sensitive information of the generated data. In some implementations, the encrypting is performed by using an external system (e.g., the external account holder system 380) associated with the account (e.g., an account of the accounting system 312), and the encrypted sensitive information is secured from access by the computing platform system (e.g., 300).

In some implementations, providing operation information from the generated data includes at least one of: preserving operational information from the generated data, providing a portion of the generated data as operation information, and generating operation information from the generated data.

In some implementations, providing a portion of the generated data includes performing redaction on at least one portion of the generated data, preserving at least one portion of the original data, and providing each preserved portion for storage (e.g., providing each preserved portion to the data warehouse 360). In some implementations, providing a portion of the generated data includes performing data deletion on at least one portion of the generated data, preserving at least one portion of the original data, and providing each preserved portion for storage (e.g., providing each preserved portion to the data warehouse 360). In some implementations, providing a portion of the generated data includes performing data encryption on at least one portion of the generated data, preserving at least one portion of the original data in an unencrypted format, and providing each preserved (unencrypted) portion for storage (e.g., providing each preserved portion to the data warehouse 360).

In some implementations, generating operation information from the generated data includes performing a data classification process as described above for S130 of FIG. 1, and providing data classifications generated by the classification process as the operation information. In some implementations, generating operation information from the generated data includes performing a data aggregation process as described above for S130 of FIG. 1, and providing aggregated data generated by the aggregation process as the operation information.

In some implementations, system operations (e.g., performed by the platform operations system 370) include at least one of usage analytics, business intelligence operations, infrastructure scaling operations, metering account usage, billing for account usage, fraud detection, error detection, general event pattern detection, platform administration operations, allocating resources, deallocating resources, cluster management operations, and auditing operations.

3.4 Storing the Moderated Data

In some implementations, the process S440 functions to control the multi-tenant computing platform system 300 to store the moderated data (e.g., the policy compliant data 354 of FIG. 3). In some implementations, the system 300 stores the moderated data at the data warehouse 360. In some implementations, the system 300 stores the moderated data at a log file storage location of the system 300 (e.g., a storage location of the storage medium 605 of FIG. 6). In some implementations, the system 300 stores the moderated data at a media records storage location of the system 300 (e.g., a storage location of the storage medium 605 of FIG. 6).

3.5 Accessing the Moderated Data

In some implementations, the process S450 functions to control the multi-tenant computing platform system 300 to access the stored moderated data. In some implementations, the platform operations system 370 accesses the stored moderated data. In some implementations, the moderated data is accessed at the data warehouse system 360. In some implementations, the moderated data is accessed at a log file storage location of the system 300. In some implementations, the moderated data is accessed at a media records storage location of the system 300

3.6 Performing System Operations

In some implementations, the process S460 functions to control the multi-tenant computing platform system 300 perform at least one system operation by using the accessed moderated data. In some implementations, the platform operations system 370 performs at least one system operation by using the accessed moderated data. In some implementations, system operations include at least one of usage analytics, business intelligence operations, infrastructure scaling operations, metering account usage, billing for account usage, fraud detection, error detection, general event pattern detection, platform administration operations, allocating resources, deallocating resources, cluster management operations, and auditing operations.

4. METHOD OF FIG. 5

The method 500 of FIG. 5 includes: moderating original data (e.g., 340) generated through operation of the computing platform system (e.g., generated through operation of the computing system 310) on behalf of an account (e.g., an account of the account system 312) of the computing platform system, the moderating being performed according to a data retention policy (e.g., 352) set for the account (process S510); and storing the moderated data (e.g., 354) at the computing platform system (process S520). The computing platform system (e.g., 300) moderates the generated data (e.g., 340) by: securing sensitive information of the generated data (e.g., 340) from access by the computing platform system (e.g., 300); and providing operational information from the generated data, the operational information being accessible by the computing platform system (e.g., 300) during performance of system operations (e.g., by the platform operations system 370).

In some implementations, the moderated data is stored at a data warehouse system (e.g., 360 of FIG. 3).

In some implementations, the method 500 includes: accessing, at the computing platform system (e.g., 300) (e.g., by using the platform operations system 370) the moderated data (e.g., 354) stored at the data warehouse system (e.g., 360) (process S530); and performing (e.g., by using the platform operations system 370) at least one system operation by using the accessed moderated data (process S540). In some implementations, system operations include at least one of usage analytics, business intelligence operations, infrastructure scaling operations, metering account usage, billing for account usage, fraud detection, error detection, general event pattern detection, platform administration operations, allocating resources, deallocating resources, cluster management operations, and auditing operations.

In some implementations, the multi-tenant computing platform system 300 performs the processes S510-S520. In some implementations, the multi-tenant computing platform system 300 performs the process S530. In some implementations, the multi-tenant computing platform system 300 performs the process S540.

In some implementations, the data retention policy engine 351 performs the process S510. In some implementations, the data manager 350 performs the process S510

In some implementations, the data retention policy engine 351 performs the process S520. In some implementations, the data warehouse system 360 performs the process S520. In some implementations, the system 300 stores the moderated data (e.g., the moderated data 354 of FIG. 3) in a storage device (e.g., the storage medium 605 of FIG. 6) of the system 300.

In some implementations, the method of FIG. 5 is similar to the method of FIG. 4. In some implementations, process S510 is similar to the process S430 of FIG. 4. In some implementations, process S520 is similar to the process S440 of FIG. 4. In some implementations, process S530 is similar to the process S450 of FIG. 4. In some implementations, process S540 is similar to the process S460 of FIG. 4.

In some implementations, the data retention policy is set for the account as described above for the process S410 of FIG. 4. In some implementations, the original data is generated as described above for the process S420.

In some implementations, the data retention policy (e.g., 352) defines actions performed by the computing platform system 300 on the data (e.g., 340) to secure the sensitive information prior to storing the data in a data warehouse (e.g., 360) of the computing platform system, and moderating data includes performing the actions defined by the data retention policy.

In some implementations, the data (e.g., 340) includes at least one of data logs, API request records, API response records, captured packets, form data input, user generated data, generated media, and obtained media.

In some implementations, actions include at least one of data redaction, data censoring, data classifying, data bucketing, data aggregating, data encryption, and partial deletion.

In some implementations, data redaction includes automatically detecting and removing at least one of a credit card number, social security number, account number, and address from the data (e.g., 340). In some implementations, data classifying includes replacing data with a generalized representation of the data. In some implementations, data aggregating includes replacing metrics of data with an aggregated representation of the metrics of data. In some implementations, data encryption includes determining an encryption callback reference (e.g., 381) for the data, transmitting the data to an external system (e.g., 380) of the encryption callback reference, and replacing the data with encrypted data provided by the external system of the encryption callback reference, wherein the account is an account for the external system (e.g., 380).

In some implementations, the computing platform system (e.g., 300) secures the sensitive information from access by the computing platform system (e.g., 300) by performing at least one of removing, censoring and encrypting of the sensitive information of the generated data. In some implementations, the computing platform system provides the operational information from the generated data by at least one of preserving operational information from the generated data and generating operation information from the generated data. In some implementations, the encrypting is performed by using an external system (e.g., 380) associated with the account, and the encrypted sensitive information is secured from access by the computing platform system (e.g., 300).

In some implementations, the encryption allows only an account holder of the account to access the encrypted sensitive information.

In some implementations, system operations include at least one of usage analytics, business intelligence operations, infrastructure scaling operations, metering account usage, billing for account usage, fraud detection, error detection, general event pattern detection, platform administration operations, allocating resources, deallocating resources, cluster management operations, and auditing operations. In some implementations, system operations include at least one of metering account usage, and billing for account usage.

In some implementations, the computing platform system performs at least one system operation by using the operational information. In some implementations, the computing platform system performs at least one system operation by using the stored moderated data. In some implementations, the moderated data is stored at a data warehouse system (e.g., 360), and the computing platform system accesses the moderated data stored at the data warehouse system and performs at least one system operation by using the accessed moderated data.

5. SYSTEM ARCHITECTURE: COMPUTING PLATFORM SYSTEM

FIG. 6 is an architecture diagram of a system (e.g., the computing platform system 300 of FIG. 3) according to an implementation in which the system is implemented by a server device. In some implementations, the system is implemented by a plurality of devices. In some implementations, the system 300 is similar to the communication platform 200 of FIG. 2.

The bus 601 interfaces with the processors 601A-601N, the main memory (e.g., a random access memory (RAM)) 622, a read only memory (ROM) 604, a processor-readable storage medium 605, a display device 607, a user input device 608, and a network device 611.

The processors 601A-601N may take many forms, such as ARM processors, X86 processors, and the like.

In some implementations, the system (e.g., 600) includes at least one of a central processing unit (processor) and a multi-processor unit (MPU).

The processors 601A-601N and the main memory 622 form a processing unit 699. In some embodiments, the processing unit includes one or more processors communicatively coupled to one or more of a RAM, ROM, and machine-readable storage medium; the one or more processors of the processing unit receive instructions stored by the one or more of a RAM, ROM, and machine-readable storage medium via a bus; and the one or more processors execute the received instructions. In some embodiments, the processing unit is an ASIC (Application-Specific Integrated Circuit). In some embodiments, the processing unit is a SoC (System-on-Chip). In some embodiments, the processing unit includes one or more of a computing system, a data manager, a data warehouse, a platform operations system, an in-flight data storage system, a post-flight data storage system, a data retention policy storage system, at least one data retention policy, in-flight data, and post-flight data.

The network adapter device 611 provides one or more wired or wireless interfaces for exchanging data and commands between the system (e.g., 600) and other devices, such as an external system (e.g., 380). Such wired and wireless interfaces include, for example, a universal serial bus (USB) interface, Bluetooth interface, Wi-Fi interface, Ethernet interface, near field communication (NFC) interface, and the like.

Machine-executable instructions in software programs (such as an operating system, application programs, and device drivers) are loaded into the memory 622 (of the processing unit 699) from the processor-readable storage medium 605, the ROM 604 or any other storage location. During execution of these software programs, the respective machine-executable instructions are accessed by at least one of processors 601A-601N (of the processing unit 699) via the bus 601, and then executed by at least one of processors 601A-601N. Data used by the software programs are also stored in the memory 622, and such data is accessed by at least one of processors 601A-601N during execution of the machine-executable instructions of the software programs. The processor-readable storage medium 605 is one of (or a combination of two or more of) a hard drive, a flash drive, a DVD, a CD, an optical disk, a floppy disk, a flash storage, a solid state drive, a ROM, an EEPROM, an electronic circuit, a semiconductor memory device, and the like. The processor-readable storage medium 605 includes machine-executable instructions (and related data) for an operating system 612, software programs 613, device drivers 614, the computing system 310, the in-flight data storage system 320, the post-flight data storage system 330, the data manager 350, and the platform operations system 370. In some implementations, the processor-readable storage medium 605 includes machine-executable instructions (and related data) for the data warehouse 360. In some implementations, the data warehouse is external to the system 300. In some implementations, the platform operations system is external to the system 300.

In some implementations, the processor-readable storage medium 605 includes in-flight data. In some implementations, the processor-readable storage medium 605 includes post-flight data. In some implementations, the processor-readable storage medium 605 includes the policy compliant (moderated) data 354. In some implementations, the processor-readable storage medium 605 includes data retention policies 615 of a plurality of accounts of the system 300 (e.g., accounts of the account system 312 of FIG. 3). In some implementations, the processor-readable storage medium 605 includes the data retention policy 352.

6. MACHINES

The systems and methods of the preferred embodiments and variations thereof can be embodied and/or implemented at least in part as a machine configured to receive a computer-readable medium storing computer-readable instructions. The instructions are preferably executed by computer-executable components preferably integrated with the computing platform. The computer-readable medium can be stored on any suitable computer-readable media such as RAMs, ROMs, flash memory, EEPROMs, optical devices (CD or DVD), hard drives, floppy drives, or any suitable device. The computer-executable component is preferably a general or application specific processor, but any suitable dedicated hardware or hardware/firmware combination device can alternatively or additionally execute the instructions.

6. CONCLUSION

As a person skilled in the art will recognize from the previous detailed description and from the figures and claims, modifications and changes can be made to the preferred embodiments of the invention without departing from the scope of this invention defined in the following claims. 

1. (canceled)
 2. A method comprising: a computing system generating original image data for a first platform account; a data manager system securing sensitive information of the original image data from access by the computing system, according to a data retention policy set for the first platform account; the data manager system providing operational information from the generated original image data of the first platform account, the operational information being accessible by a platform operations system during performance of system operations by the platform operations system.
 3. The method of claim 1, wherein the data retention policy defines actions to secure the sensitive information, and wherein securing sensitive information comprises performing the actions defined by the data retention policy, and wherein actions include at least one of data redaction, data censoring, data classifying, data bucketing, data aggregating, data encryption, and partial deletion.
 4. The method of claim 1, wherein securing sensitive information includes data classifying.
 5. The method of claim 1, wherein securing sensitive information includes data bucketing.
 6. The method of claim 1, wherein securing sensitive information includes data aggregating.
 7. The method of claim 1, wherein system operations include usage analytics.
 8. The method of claim 1, wherein system operations include business intelligence.
 9. The method of claim 1, wherein system operations include metering account usage.
 10. The method of claim 1, wherein system operations include pattern detection.
 11. The method of claim 1, wherein system operations include fraud detection.
 12. A method comprising: a computing system generating original audio data for a first platform account; a data manager system securing sensitive information of the original audio data from access by the computing system, according to a data retention policy set for the first platform account; the data manager system providing operational information from the generated original audio data of the first platform account, the operational information being accessible by a platform operations system during performance of system operations by the platform operations system.
 13. The method of claim 11, wherein the data retention policy defines actions to secure the sensitive information, and wherein securing sensitive information comprises performing the actions defined by the data retention policy, and wherein actions include at least one of data redaction, data censoring, data classifying, data bucketing, data aggregating, data encryption, and partial deletion.
 14. The method of claim 11, wherein securing sensitive information includes data classifying.
 15. The method of claim 11, wherein securing sensitive information includes data bucketing.
 16. The method of claim 11, wherein securing sensitive information includes data aggregating.
 17. The method of claim 11, wherein system operations include usage analytics.
 18. The method of claim 11, wherein system operations include business intelligence.
 19. The method of claim 11, wherein system operations include metering account usage.
 20. The method of claim 11, wherein system operations include pattern detection.
 21. The method of claim 11, wherein system operations include fraud detection. 